@theadrianmckennashow: Drone time

The AdrianMcKenna show

Open In TikTok:

Region: IE

Monday 28 September 2026 17:22:44 GMT

12339

374

22

24

Music

Download

No Watermark .mp4 (27.1MB) No Watermark(HD) .mp4 (21.5MB) Watermark .mp4 (27.1MB) Music .mp3

Comments

shane :

what drone is it . looks great

2026-09-28 18:18:33

1

✝️🇮🇪Charlie🇮🇪✝️ :

Gowan Jake boiiii

2026-09-29 21:23:40

0

scan :

Use the aluminium troughs in the valley way cheaper than lead, and better 👍

2026-09-28 20:01:48

0

user1400024159778 :

Give me a loan of that Wil ya 😂🥰

2026-09-28 18:16:47

2

jason :

Young man show his dad how its done . 😁👏👏

2026-09-30 08:42:01

0

user80628355491465 :

well done great job 👍👍

2026-09-28 17:37:09

2

irishP :

Good man great to be able to drive it so good and so young well done

2026-09-28 18:48:03

1

markconnell :

Yupa Jake 😁

2026-09-30 18:01:34

0

stephen fehilly :

Flying keep it going

2026-09-28 17:49:51

1

Breda Walsh :

grearjob

2026-09-28 18:18:37

1

miss Lou o brien ✨✨ :

well done yr doing a good job keep it up fk the haters love Ur content 👍

2026-09-28 22:23:02

0

jh ♛ :

Good man Jake boi

2026-09-29 21:20:15

0

Ac’ :

@🏎️🇮🇪jakecorsa🇮🇪🏎️ go on fella

2026-09-28 17:40:30

1

JP Paving and Landscaping :

👍

2026-09-28 18:33:10

1

UnknownCarrera 🤷‍♂️👌 :

🥰🥰🥰

2026-09-28 17:29:10

0

user2996434357663 :

👍👍👍

2026-09-29 20:46:35

0

To see more videos from user @theadrianmckennashow, please go to the Tikwm homepage.

Other Videos

Heaa!🤍 #foryou #unfrezzmyaccount #vairal #tending #storyvideo

Heaa!🤍 #foryou #unfrezzmyaccount #vairal #tending #storyvideo

#コンカフェ嬢 #流川コンカフェ

#コンカフェ嬢 #流川コンカフェ

Bánh Bông lan trứng muối không phải cứ thật đẫm mới ngon đâu nha mọi người. Cái quan trọng là mình phải biết làm sao cho chiếc bánh cân bằng giữa các phần topping với nhau nữa đó. #vykhuebakinglab #bonglantrungmuoi #vykhuefoodstylist

Bánh Bông lan trứng muối không phải cứ thật đẫm mới ngon đâu nha mọi người. Cái quan trọng là mình phải biết làm sao cho chiếc bánh cân bằng giữa các phần topping với nhau nữa đó. #vykhuebakinglab #bonglantrungmuoi #vykhuefoodstylist

Want to break into Quant, ML, or Quantitative Research? Don’t build another Titanic notebook. Build projects that combine: Mathematics + Statistics + Finance + Machine Learning + Software Engineering 🟢 BEGINNER 1️⃣ Stock Price Analysis Dashboard Analyze returns, volatility, drawdowns, and correlations. 2️⃣ Portfolio Optimization Build portfolios using risk/return optimization and compare different asset allocations. 3️⃣ Factor Analysis Study factors such as momentum, value, size, and volatility. 4️⃣ Risk Metrics Engine Calculate and visualize: → Volatility → Sharpe Ratio → Maximum Drawdown → VaR → Expected Shortfall 🟡 INTERMEDIATE 5️⃣ ML Stock Return Predictor 🤖 Predict future returns using engineered market features. Compare: → Linear Regression → Random Forest → XGBoost → Neural Networks 6️⃣ Algorithmic Trading Strategy Backtester Build: Market Data → Signals → Position Sizing → Risk Management → Backtesting → Performance Analysis 7️⃣ Pairs Trading System Identify correlated assets and trade deviations from their historical relationship. Include: → Cointegration → Spread calculation → Z-score → Entry/exit rules → Transaction costs 8️⃣ Volatility Forecasting Predict future volatility using: → Historical volatility → GARCH → ML models → Time-series features 🔴 ADVANCED 9️⃣ ML-Based Anomaly Detection for Markets Detect unusual: → Price movements → Trading volume → Volatility → Market regimes Try: Isolation Forest + Autoencoders + Statistical Methods 🔟 Market Regime Detection Classify markets into: 📈 Bull 📉 Bear 〰️ Sideways ⚡ High-volatility Use clustering, Hidden Markov Models, or ML. 1️⃣1️⃣ Limit Order Book Prediction Use order-book features to predict short-term price movement. Explore: → Bid/ask imbalance → Spread → Depth → Microstructure features → Deep learning 1️⃣2️⃣ Reinforcement Learning Trading Agent 🧠 Train an agent to make portfolio decisions based on: State → Action → Reward → New State Compare it against traditional strategies. 🧠 QUANT + AI 1️⃣3️⃣ LLM Financial Research Assistant Build a RAG system that can analyze financial documents and generate cited research. 1️⃣4️⃣ AI Portfolio Risk Assistant Combine portfolio analytics with an LLM interface that explains: → Risk exposure → Drawdowns → Portfolio concentration → Scenario analysis 1️⃣5️⃣ Multi-Agent Quant Research System Agents handle: 📊 Data analysis 📈 Technical research 💰 Fundamental research 🧮 Quantitative signals 🛡️ Risk analysis Then combine their outputs into a research report. 🚀 MAKE THE PROJECT PORTFOLIO-READY Don’t stop at: ❌ “My model achieved 82% accuracy.” Build: Data ↓ Cleaning ↓ Feature Engineering ↓ Statistical Analysis ↓ Strategy / ML Model ↓ Backtesting ↓ Transaction Costs ↓ Risk Management ↓ Performance Evaluation ↓ API / Dashboard ↓ Documentation 🎯 BEST 5 FOR A SERIOUS PORTFOLIO 🥇 Portfolio Optimization 🥈 Pairs Trading System 🥉 ML Return Prediction 🏅 Market Regime Detection 🏆 End-to-End Algorithmic Trading Platform 💡 Quant isn’t just about predicting prices. It’s about understanding: Probability + Statistics + Optimization + Risk + Markets + Algorithms + Software Build projects that demonstrate all of them. ⚠️ Backtests are research tools, not guarantees of future returns. Always account for transaction costs, slippage, look-ahead bias, and overfitting. 📌 Save this roadmap if you’re building a Quant + ML portfolio. #Quant #QuantFinance #MachineLearning #AlgorithmicTrading #creatorsearchinsights

Want to break into Quant, ML, or Quantitative Research? Don’t build another Titanic notebook. Build projects that combine: Mathematics + Statistics + Finance + Machine Learning + Software Engineering 🟢 BEGINNER 1️⃣ Stock Price Analysis Dashboard Analyze returns, volatility, drawdowns, and correlations. 2️⃣ Portfolio Optimization Build portfolios using risk/return optimization and compare different asset allocations. 3️⃣ Factor Analysis Study factors such as momentum, value, size, and volatility. 4️⃣ Risk Metrics Engine Calculate and visualize: → Volatility → Sharpe Ratio → Maximum Drawdown → VaR → Expected Shortfall 🟡 INTERMEDIATE 5️⃣ ML Stock Return Predictor 🤖 Predict future returns using engineered market features. Compare: → Linear Regression → Random Forest → XGBoost → Neural Networks 6️⃣ Algorithmic Trading Strategy Backtester Build: Market Data → Signals → Position Sizing → Risk Management → Backtesting → Performance Analysis 7️⃣ Pairs Trading System Identify correlated assets and trade deviations from their historical relationship. Include: → Cointegration → Spread calculation → Z-score → Entry/exit rules → Transaction costs 8️⃣ Volatility Forecasting Predict future volatility using: → Historical volatility → GARCH → ML models → Time-series features 🔴 ADVANCED 9️⃣ ML-Based Anomaly Detection for Markets Detect unusual: → Price movements → Trading volume → Volatility → Market regimes Try: Isolation Forest + Autoencoders + Statistical Methods 🔟 Market Regime Detection Classify markets into: 📈 Bull 📉 Bear 〰️ Sideways ⚡ High-volatility Use clustering, Hidden Markov Models, or ML. 1️⃣1️⃣ Limit Order Book Prediction Use order-book features to predict short-term price movement. Explore: → Bid/ask imbalance → Spread → Depth → Microstructure features → Deep learning 1️⃣2️⃣ Reinforcement Learning Trading Agent 🧠 Train an agent to make portfolio decisions based on: State → Action → Reward → New State Compare it against traditional strategies. 🧠 QUANT + AI 1️⃣3️⃣ LLM Financial Research Assistant Build a RAG system that can analyze financial documents and generate cited research. 1️⃣4️⃣ AI Portfolio Risk Assistant Combine portfolio analytics with an LLM interface that explains: → Risk exposure → Drawdowns → Portfolio concentration → Scenario analysis 1️⃣5️⃣ Multi-Agent Quant Research System Agents handle: 📊 Data analysis 📈 Technical research 💰 Fundamental research 🧮 Quantitative signals 🛡️ Risk analysis Then combine their outputs into a research report. 🚀 MAKE THE PROJECT PORTFOLIO-READY Don’t stop at: ❌ “My model achieved 82% accuracy.” Build: Data ↓ Cleaning ↓ Feature Engineering ↓ Statistical Analysis ↓ Strategy / ML Model ↓ Backtesting ↓ Transaction Costs ↓ Risk Management ↓ Performance Evaluation ↓ API / Dashboard ↓ Documentation 🎯 BEST 5 FOR A SERIOUS PORTFOLIO 🥇 Portfolio Optimization 🥈 Pairs Trading System 🥉 ML Return Prediction 🏅 Market Regime Detection 🏆 End-to-End Algorithmic Trading Platform 💡 Quant isn’t just about predicting prices. It’s about understanding: Probability + Statistics + Optimization + Risk + Markets + Algorithms + Software Build projects that demonstrate all of them. ⚠️ Backtests are research tools, not guarantees of future returns. Always account for transaction costs, slippage, look-ahead bias, and overfitting. 📌 Save this roadmap if you’re building a Quant + ML portfolio. #Quant #QuantFinance #MachineLearning #AlgorithmicTrading #creatorsearchinsights

I miss you bhaiya...!!🥺✈️ #CapCut #fypシ゚ #ইনশাআল্লাহ_যাবে_foryou_তে।

I miss you bhaiya...!!🥺✈️ #CapCut #fypシ゚ #ইনশাআল্লাহ_যাবে_foryou_তে।

About

Robot
API

Legal

Privacy Policy